Esempi in Python per get_signals

Esempio n. 1

File: hmmc_tf.py Progetto: ShuvenduBikash/machine_learning_examples

def fake_signal():
    signals = get_signals()
    # assume all signals are same length
    signals = np.array(signals)
    hmm = HMM(5, 3, signals[0].shape[1]) # M, K, D
    hmm.init_random(signals)

    init = tf.global_variables_initializer()
    session = tf.InteractiveSession()
    session.run(init)
    hmm.set_session(session)

    hmm.fit(signals, max_iter=30)
    L = hmm.get_cost_multi(signals).sum()
    print("LL for fitted params:", L)

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = big_init()

    # turn these into their "pre-softmax" forms
    pi = np.log(pi)
    A = np.log(A)
    R = np.log(R)
    M, K, D, _ = sigma.shape # need to convert full cov into diag cov
    logSigma = np.zeros((M, K, D))
    for j in range(M):
        for k in range(D):
            logSigma[j,k] = np.log(np.diag(sigma[j,k]))

    hmm.set(pi, A, R, mu, logSigma)
    L = hmm.get_cost_multi(signals).sum()
    print("LL for actual params:", L)

Esempio n. 2

File: hmmc_tf.py Progetto: zivzone/machine_learning_examples

def fake_signal():
    signals = get_signals()
    # assume all signals are same length
    signals = np.array(signals)
    hmm = HMM(5, 3, signals[0].shape[1]) # M, K, D
    hmm.init_random(signals)

    init = tf.global_variables_initializer()
    session = tf.InteractiveSession()
    session.run(init)
    hmm.set_session(session)

    hmm.fit(signals, max_iter=30)
    L = hmm.get_cost_multi(signals).sum()
    print "LL for fitted params:", L

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = big_init()

    # turn these into their "pre-softmax" forms
    pi = np.log(pi)
    A = np.log(A)
    R = np.log(R)
    M, K, D, _ = sigma.shape # need to convert full cov into diag cov
    logSigma = np.zeros((M, K, D))
    for j in range(M):
        for k in range(D):
            logSigma[j,k] = np.log(np.diag(sigma[j,k]))

    hmm.set(pi, A, R, mu, logSigma)
    L = hmm.get_cost_multi(signals).sum()
    print "LL for actual params:", L

Esempio n. 3

def fake_signal(init=big_init):
    signals = get_signals(init=init)
    # for signal in signals:
    #     for d in xrange(signal.shape[1]):
    #         plt.plot(signal[:,d])
    # plt.show()

    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()

Esempio n. 4

File: hmmc_theano.py Progetto: AndreyDrv/machine_learning_examples

def fake_signal():
    signals = get_signals()
    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for fitted params:", L

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = big_init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for actual params:", L

Esempio n. 5

File: hmmc_theano.py Progetto: owainr/HMM

def fake_signal():
    signals = get_signals()
    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for fitted params:", L

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = big_init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for actual params:", L

Esempio n. 6

def fake_signal():
    signals = get_signals()
    # assume all signals are same length
    signals = np.array(signals)
    hmm = HMM(5, 3, signals[0].shape[1]) # M, K, D
    hmm.init_random(signals)

    init = tf.global_variables_initializer()
    session = tf.InteractiveSession()
    session.run(init)
    hmm.set_session(session)

    hmm.fit(signals, max_iter=30)
    L = hmm.get_cost_multi(signals).sum()

Esempio n. 7

File: hmmc_concat.py Progetto: souviksamanta95/machine_learning_examples

def fake_signal(init=simple_init):
    signals = get_signals(N=10, T=10, init=init)
    # for signal in signals:
    #     for d in xrange(signal.shape[1]):
    #         plt.plot(signal[:,d])
    # plt.show()

    hmm = HMM(2, 2)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for fitted params:", L)

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for actual params:", L)

Esempio n. 8

File: hmmc.py Progetto: ShuvenduBikash/machine_learning_examples

def fake_signal(init=simple_init):
    signals = get_signals(N=10, T=10, init=init)
    # for signal in signals:
    #     for d in xrange(signal.shape[1]):
    #         plt.plot(signal[:,d])
    # plt.show()

    hmm = HMM(2, 2)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for fitted params:", L

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for actual params:", L

Esempio n. 9

File: hmmc_scaled.py Progetto: AndreyDrv/machine_learning_examples

def fake_signal(init=big_init):
    signals = get_signals(init=init)
    for signal in signals:
        for d in xrange(signal.shape[1]):
            plt.plot(signal[:,d])
    plt.show()

    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for fitted params:", L

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for actual params:", L

Esempio n. 10

def fake_signal(init=simple_init):
    signals = get_signals(N=10, T=10, init=init)

    hmm = HMM(2, 2)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for fitted params:", L)
    for signal in signals:
        plt.plot(signal, '--')
        plt.plot(hmm.predict_sequence(signal))
    plt.show()
    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for actual params:", L)

    # print most likely state sequence
    print("Most likely state sequence for initial observation:")
    print(hmm.predict_sequence(signals[0]))

Esempio n. 11

def fake_signal(init=big_init):
    signals = get_signals(init=init)
    # for signal in signals:
    #   print "mean:", signal.mean(axis=0), "var:", signal.var(axis=0)

    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for fitted params:", L

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    M, K, D = mu.shape
    diag_sigma = np.zeros((M, K, D))
    for j in xrange(M):
        for k in xrange(K):
            diag_sigma[j, k] = np.diag(sigma[j, k])
    hmm.set(pi, A, R, mu, diag_sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for actual params:", L

Esempio n. 12

File: hmmc_scaled_diag.py Progetto: AndreyDrv/machine_learning_examples

def fake_signal(init=big_init):
    signals = get_signals(init=init)
    # for signal in signals:
    #   print "mean:", signal.mean(axis=0), "var:", signal.var(axis=0)

    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for fitted params:", L

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    M, K, D = mu.shape
    diag_sigma = np.zeros((M, K, D))
    for j in xrange(M):
        for k in xrange(K):
            diag_sigma[j,k] = np.diag(sigma[j,k])
    hmm.set(pi, A, R, mu, diag_sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print "LL for actual params:", L

Esempio n. 13

File: hmmc_theano2.py Progetto: Sebster10/Pandas-Practice

def fake_signal():
    signals = get_signals()
    hmm = HMM(5, 3)
    hmm.fit(signals, max_iter=3)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for fitted params:", L)

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = big_init()

    # turn these into their "pre-softmax" forms
    pi = np.log(pi)
    A = np.log(A)
    R = np.log(R)

    # decompose sigma using cholesky factorization
    sigma = np.linalg.cholesky(sigma)

    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for actual params:", L)

Esempio n. 14

def fake_signal(init=big_init):
    signals = get_signals(init=init)
    # for signal in signals:
    #     for d in xrange(signal.shape[1]):
    #         plt.plot(signal[:,d])
    # plt.show()

    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for fitted params:", L)

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for actual params:", L)

    # print most likely state sequence
    print("Most likely state sequence for initial observation:")
    print(hmm.get_state_sequence(signals[0]))

Esempio n. 15

File: hmmc.py Progetto: cmagnusb/machine_learning_examples

def fake_signal(init=simple_init):
    signals = get_signals(N=10, T=10, init=init)
    # for signal in signals:
    #     for d in xrange(signal.shape[1]):
    #         plt.plot(signal[:,d])
    # plt.show()

    hmm = HMM(2, 2)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for fitted params:", L)

    # test in actual params
    _, _, _, pi, A, R, mu, sigma = init()
    hmm.set(pi, A, R, mu, sigma)
    L = hmm.log_likelihood_multi(signals).sum()
    print("LL for actual params:", L)

    # print most likely state sequence
    print("Most likely state sequence for initial observation:")
    print(hmm.get_state_sequence(signals[0]))

Esempio n. 16

def fake_signal():
    signals = get_signals()
    hmm = HMM(5, 3)
    hmm.fit(signals)
    L = hmm.log_likelihood_multi(signals).sum()